{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algerian Forest Fires Dataset \n",
    "Data Set Information:\n",
    "\n",
    "The dataset includes 244 instances that regroup a data of two regions of Algeria,namely the Bejaia region located in the northeast of Algeria and the Sidi Bel-abbes region located in the northwest of Algeria.\n",
    "\n",
    "122 instances for each region.\n",
    "\n",
    "The period from June 2012 to September 2012.\n",
    "The dataset includes 11 attribues and 1 output attribue (class)\n",
    "The 244 instances have been classified into fire(138 classes) and not fire (106 classes) classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attribute Information:\n",
    "\n",
    "1. Date : (DD/MM/YYYY) Day, month ('june' to 'september'), year (2012)\n",
    "Weather data observations\n",
    "2. Temp : temperature noon (temperature max) in Celsius degrees: 22 to 42\n",
    "3. RH : Relative Humidity in %: 21 to 90\n",
    "4. Ws :Wind speed in km/h: 6 to 29\n",
    "5. Rain: total day in mm: 0 to 16.8\n",
    "FWI Components\n",
    "6. Fine Fuel Moisture Code (FFMC) index from the FWI system: 28.6 to 92.5\n",
    "7. Duff Moisture Code (DMC) index from the FWI system: 1.1 to 65.9\n",
    "8. Drought Code (DC) index from the FWI system: 7 to 220.4\n",
    "9. Initial Spread Index (ISI) index from the FWI system: 0 to 18.5\n",
    "10. Buildup Index (BUI) index from the FWI system: 1.1 to 68\n",
    "11. Fire Weather Index (FWI) Index: 0 to 31.1\n",
    "12. Classes: two classes, namely Fire and not Fire"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('Algerian_forest_fires_dataset_UPDATE.csv' ,header=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>not fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>not fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0</td>\n",
       "      <td>not fire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  day month  year Temperature  RH  Ws Rain   FFMC  DMC    DC  ISI  BUI  FWI  \\\n",
       "0  01    06  2012          29  57  18     0  65.7  3.4   7.6  1.3  3.4  0.5   \n",
       "1  02    06  2012          29  61  13   1.3  64.4  4.1   7.6    1  3.9  0.4   \n",
       "2  03    06  2012          26  82  22  13.1  47.1  2.5   7.1  0.3  2.7  0.1   \n",
       "3  04    06  2012          25  89  13   2.5  28.6  1.3   6.9    0  1.7    0   \n",
       "4  05    06  2012          27  77  16     0  64.8    3  14.2  1.2  3.9  0.5   \n",
       "\n",
       "     Classes    \n",
       "0  not fire     \n",
       "1  not fire     \n",
       "2  not fire     \n",
       "3  not fire     \n",
       "4  not fire     "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 246 entries, 0 to 245\n",
      "Data columns (total 14 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   day          246 non-null    object\n",
      " 1   month        245 non-null    object\n",
      " 2   year         245 non-null    object\n",
      " 3   Temperature  245 non-null    object\n",
      " 4    RH          245 non-null    object\n",
      " 5    Ws          245 non-null    object\n",
      " 6   Rain         245 non-null    object\n",
      " 7   FFMC         245 non-null    object\n",
      " 8   DMC          245 non-null    object\n",
      " 9   DC           245 non-null    object\n",
      " 10  ISI          245 non-null    object\n",
      " 11  BUI          245 non-null    object\n",
      " 12  FWI          245 non-null    object\n",
      " 13  Classes      244 non-null    object\n",
      "dtypes: object(14)\n",
      "memory usage: 27.0+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Sidi-Bel Abbes Region Dataset</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>14</td>\n",
       "      <td>07</td>\n",
       "      <td>2012</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>18</td>\n",
       "      <td>0.2</td>\n",
       "      <td>88.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>14.6 9</td>\n",
       "      <td>12.5</td>\n",
       "      <td>10.4</td>\n",
       "      <td>fire</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               day month  year Temperature   RH   Ws Rain   \\\n",
       "122  Sidi-Bel Abbes Region Dataset   NaN   NaN         NaN  NaN  NaN   NaN   \n",
       "167                             14    07  2012          37   37   18   0.2   \n",
       "\n",
       "     FFMC   DMC      DC   ISI   BUI      FWI Classes    \n",
       "122   NaN   NaN     NaN   NaN   NaN      NaN       NaN  \n",
       "167  88.9  12.9  14.6 9  12.5  10.4  fire          NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## missing values\n",
    "dataset[dataset.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is converted into two sets based on Region from 122th index, we can make a new column based on the Region\n",
    "\n",
    "1 : \"Bejaia Region Dataset\"\n",
    "\n",
    "2 : \"Sidi-Bel Abbes Region Dataset\"\n",
    "\n",
    "Add new column with region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.loc[:122,\"Region\"]=0\n",
    "dataset.loc[122:,\"Region\"]=1\n",
    "df=dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 246 entries, 0 to 245\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   day          246 non-null    object \n",
      " 1   month        245 non-null    object \n",
      " 2   year         245 non-null    object \n",
      " 3   Temperature  245 non-null    object \n",
      " 4    RH          245 non-null    object \n",
      " 5    Ws          245 non-null    object \n",
      " 6   Rain         245 non-null    object \n",
      " 7   FFMC         245 non-null    object \n",
      " 8   DMC          245 non-null    object \n",
      " 9   DC           245 non-null    object \n",
      " 10  ISI          245 non-null    object \n",
      " 11  BUI          245 non-null    object \n",
      " 12  FWI          245 non-null    object \n",
      " 13  Classes      244 non-null    object \n",
      " 14  Region       246 non-null    float64\n",
      "dtypes: float64(1), object(14)\n",
      "memory usage: 29.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['Region']]=df[['Region']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  day month  year Temperature  RH  Ws Rain   FFMC  DMC    DC  ISI  BUI  FWI  \\\n",
       "0  01    06  2012          29  57  18     0  65.7  3.4   7.6  1.3  3.4  0.5   \n",
       "1  02    06  2012          29  61  13   1.3  64.4  4.1   7.6    1  3.9  0.4   \n",
       "2  03    06  2012          26  82  22  13.1  47.1  2.5   7.1  0.3  2.7  0.1   \n",
       "3  04    06  2012          25  89  13   2.5  28.6  1.3   6.9    0  1.7    0   \n",
       "4  05    06  2012          27  77  16     0  64.8    3  14.2  1.2  3.9  0.5   \n",
       "\n",
       "     Classes    Region  \n",
       "0  not fire          0  \n",
       "1  not fire          0  \n",
       "2  not fire          0  \n",
       "3  not fire          0  \n",
       "4  not fire          0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day            0\n",
       "month          1\n",
       "year           1\n",
       "Temperature    1\n",
       " RH            1\n",
       " Ws            1\n",
       "Rain           1\n",
       "FFMC           1\n",
       "DMC            1\n",
       "DC             1\n",
       "ISI            1\n",
       "BUI            1\n",
       "FWI            1\n",
       "Classes        2\n",
       "Region         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Removing the null values\n",
    "df=df.dropna().reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  day month  year Temperature  RH  Ws Rain   FFMC  DMC    DC  ISI  BUI  FWI  \\\n",
       "0  01    06  2012          29  57  18     0  65.7  3.4   7.6  1.3  3.4  0.5   \n",
       "1  02    06  2012          29  61  13   1.3  64.4  4.1   7.6    1  3.9  0.4   \n",
       "2  03    06  2012          26  82  22  13.1  47.1  2.5   7.1  0.3  2.7  0.1   \n",
       "3  04    06  2012          25  89  13   2.5  28.6  1.3   6.9    0  1.7    0   \n",
       "4  05    06  2012          27  77  16     0  64.8    3  14.2  1.2  3.9  0.5   \n",
       "\n",
       "     Classes    Region  \n",
       "0  not fire          0  \n",
       "1  not fire          0  \n",
       "2  not fire          0  \n",
       "3  not fire          0  \n",
       "4  not fire          0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day            0\n",
       "month          0\n",
       "year           0\n",
       "Temperature    0\n",
       " RH            0\n",
       " Ws            0\n",
       "Rain           0\n",
       "FFMC           0\n",
       "DMC            0\n",
       "DC             0\n",
       "ISI            0\n",
       "BUI            0\n",
       "FWI            0\n",
       "Classes        0\n",
       "Region         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>day</th>\n",
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       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
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       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>day</td>\n",
       "      <td>month</td>\n",
       "      <td>year</td>\n",
       "      <td>Temperature</td>\n",
       "      <td>RH</td>\n",
       "      <td>Ws</td>\n",
       "      <td>Rain</td>\n",
       "      <td>FFMC</td>\n",
       "      <td>DMC</td>\n",
       "      <td>DC</td>\n",
       "      <td>ISI</td>\n",
       "      <td>BUI</td>\n",
       "      <td>FWI</td>\n",
       "      <td>Classes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     day  month  year  Temperature   RH   Ws  Rain   FFMC  DMC  DC  ISI  BUI  \\\n",
       "122  day  month  year  Temperature   RH   Ws  Rain   FFMC  DMC  DC  ISI  BUI   \n",
       "\n",
       "     FWI  Classes    Region  \n",
       "122  FWI  Classes         1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[122]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "##remove the 122nd row\n",
    "df=df.drop(122).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>01</td>\n",
       "      <td>06</td>\n",
       "      <td>2012</td>\n",
       "      <td>32</td>\n",
       "      <td>71</td>\n",
       "      <td>12</td>\n",
       "      <td>0.7</td>\n",
       "      <td>57.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.2</td>\n",
       "      <td>not fire</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    day month  year Temperature  RH  Ws Rain   FFMC  DMC   DC  ISI  BUI  FWI  \\\n",
       "122  01    06  2012          32  71  12   0.7  57.1  2.5  8.2  0.6  2.8  0.2   \n",
       "\n",
       "       Classes    Region  \n",
       "122  not fire          1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[122]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'month', 'year', 'Temperature', ' RH', ' Ws', 'Rain ', 'FFMC',\n",
       "       'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes  ', 'Region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'month', 'year', 'Temperature', 'RH', 'Ws', 'Rain', 'FFMC',\n",
       "       'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes', 'Region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## fix spaces in columns names\n",
    "df.columns=df.columns.str.strip()\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 243 entries, 0 to 242\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   day          243 non-null    object\n",
      " 1   month        243 non-null    object\n",
      " 2   year         243 non-null    object\n",
      " 3   Temperature  243 non-null    object\n",
      " 4   RH           243 non-null    object\n",
      " 5   Ws           243 non-null    object\n",
      " 6   Rain         243 non-null    object\n",
      " 7   FFMC         243 non-null    object\n",
      " 8   DMC          243 non-null    object\n",
      " 9   DC           243 non-null    object\n",
      " 10  ISI          243 non-null    object\n",
      " 11  BUI          243 non-null    object\n",
      " 12  FWI          243 non-null    object\n",
      " 13  Classes      243 non-null    object\n",
      " 14  Region       243 non-null    int32 \n",
      "dtypes: int32(1), object(14)\n",
      "memory usage: 27.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changes the required columns as integer data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'month', 'year', 'Temperature', 'RH', 'Ws', 'Rain', 'FFMC',\n",
       "       'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes', 'Region'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['month','day','year','Temperature','RH','Ws']]=df[['month','day','year','Temperature','RH','Ws']].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 243 entries, 0 to 242\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   day          243 non-null    int32 \n",
      " 1   month        243 non-null    int32 \n",
      " 2   year         243 non-null    int32 \n",
      " 3   Temperature  243 non-null    int32 \n",
      " 4   RH           243 non-null    int32 \n",
      " 5   Ws           243 non-null    int32 \n",
      " 6   Rain         243 non-null    object\n",
      " 7   FFMC         243 non-null    object\n",
      " 8   DMC          243 non-null    object\n",
      " 9   DC           243 non-null    object\n",
      " 10  ISI          243 non-null    object\n",
      " 11  BUI          243 non-null    object\n",
      " 12  FWI          243 non-null    object\n",
      " 13  Classes      243 non-null    object\n",
      " 14  Region       243 non-null    int32 \n",
      "dtypes: int32(7), object(8)\n",
      "memory usage: 22.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing the other columns to float data datatype\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "objects=[features for features in df.columns if df[features].dtypes=='O']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in objects:\n",
    "    if i!='Classes':\n",
    "        df[i]=df[i].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 243 entries, 0 to 242\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   day          243 non-null    int32  \n",
      " 1   month        243 non-null    int32  \n",
      " 2   year         243 non-null    int32  \n",
      " 3   Temperature  243 non-null    int32  \n",
      " 4   RH           243 non-null    int32  \n",
      " 5   Ws           243 non-null    int32  \n",
      " 6   Rain         243 non-null    float64\n",
      " 7   FFMC         243 non-null    float64\n",
      " 8   DMC          243 non-null    float64\n",
      " 9   DC           243 non-null    float64\n",
      " 10  ISI          243 non-null    float64\n",
      " 11  BUI          243 non-null    float64\n",
      " 12  FWI          243 non-null    float64\n",
      " 13  Classes      243 non-null    object \n",
      " 14  Region       243 non-null    int32  \n",
      "dtypes: float64(7), int32(7), object(1)\n",
      "memory usage: 22.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Rain', 'FFMC', 'DMC', 'DC', 'ISI', 'BUI', 'FWI', 'Classes']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
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       "      <th>DMC</th>\n",
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       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.0</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>243.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15.761317</td>\n",
       "      <td>7.502058</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>32.152263</td>\n",
       "      <td>62.041152</td>\n",
       "      <td>15.493827</td>\n",
       "      <td>0.762963</td>\n",
       "      <td>77.842387</td>\n",
       "      <td>14.680658</td>\n",
       "      <td>49.430864</td>\n",
       "      <td>4.742387</td>\n",
       "      <td>16.690535</td>\n",
       "      <td>7.035391</td>\n",
       "      <td>0.497942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.842552</td>\n",
       "      <td>1.114793</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.628039</td>\n",
       "      <td>14.828160</td>\n",
       "      <td>2.811385</td>\n",
       "      <td>2.003207</td>\n",
       "      <td>14.349641</td>\n",
       "      <td>12.393040</td>\n",
       "      <td>47.665606</td>\n",
       "      <td>4.154234</td>\n",
       "      <td>14.228421</td>\n",
       "      <td>7.440568</td>\n",
       "      <td>0.501028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.600000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.850000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>12.350000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>83.300000</td>\n",
       "      <td>11.300000</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>12.400000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>88.300000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>69.100000</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>22.650000</td>\n",
       "      <td>11.450000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>65.900000</td>\n",
       "      <td>220.400000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              day       month    year  Temperature          RH          Ws  \\\n",
       "count  243.000000  243.000000   243.0   243.000000  243.000000  243.000000   \n",
       "mean    15.761317    7.502058  2012.0    32.152263   62.041152   15.493827   \n",
       "std      8.842552    1.114793     0.0     3.628039   14.828160    2.811385   \n",
       "min      1.000000    6.000000  2012.0    22.000000   21.000000    6.000000   \n",
       "25%      8.000000    7.000000  2012.0    30.000000   52.500000   14.000000   \n",
       "50%     16.000000    8.000000  2012.0    32.000000   63.000000   15.000000   \n",
       "75%     23.000000    8.000000  2012.0    35.000000   73.500000   17.000000   \n",
       "max     31.000000    9.000000  2012.0    42.000000   90.000000   29.000000   \n",
       "\n",
       "             Rain        FFMC         DMC          DC         ISI         BUI  \\\n",
       "count  243.000000  243.000000  243.000000  243.000000  243.000000  243.000000   \n",
       "mean     0.762963   77.842387   14.680658   49.430864    4.742387   16.690535   \n",
       "std      2.003207   14.349641   12.393040   47.665606    4.154234   14.228421   \n",
       "min      0.000000   28.600000    0.700000    6.900000    0.000000    1.100000   \n",
       "25%      0.000000   71.850000    5.800000   12.350000    1.400000    6.000000   \n",
       "50%      0.000000   83.300000   11.300000   33.100000    3.500000   12.400000   \n",
       "75%      0.500000   88.300000   20.800000   69.100000    7.250000   22.650000   \n",
       "max     16.800000   96.000000   65.900000  220.400000   19.000000   68.000000   \n",
       "\n",
       "              FWI      Region  \n",
       "count  243.000000  243.000000  \n",
       "mean     7.035391    0.497942  \n",
       "std      7.440568    0.501028  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.700000    0.000000  \n",
       "50%      4.200000    0.000000  \n",
       "75%     11.450000    1.000000  \n",
       "max     31.100000    1.000000  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day  month  year  Temperature  RH  Ws  Rain  FFMC  DMC    DC  ISI  BUI  \\\n",
       "0    1      6  2012           29  57  18   0.0  65.7  3.4   7.6  1.3  3.4   \n",
       "1    2      6  2012           29  61  13   1.3  64.4  4.1   7.6  1.0  3.9   \n",
       "2    3      6  2012           26  82  22  13.1  47.1  2.5   7.1  0.3  2.7   \n",
       "3    4      6  2012           25  89  13   2.5  28.6  1.3   6.9  0.0  1.7   \n",
       "4    5      6  2012           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9   \n",
       "\n",
       "   FWI      Classes  Region  \n",
       "0  0.5  not fire          0  \n",
       "1  0.4  not fire          0  \n",
       "2  0.1  not fire          0  \n",
       "3  0.0  not fire          0  \n",
       "4  0.5  not fire          0  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Let ave the cleaned dataset\n",
    "df.to_csv('Algerian_forest_fires_cleaned_dataset.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "## drop day,month and year\n",
    "df_copy=df.drop(['day','month','year'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  RH  Ws  Rain  FFMC  DMC    DC  ISI  BUI  FWI      Classes  \\\n",
       "0           29  57  18   0.0  65.7  3.4   7.6  1.3  3.4  0.5  not fire      \n",
       "1           29  61  13   1.3  64.4  4.1   7.6  1.0  3.9  0.4  not fire      \n",
       "2           26  82  22  13.1  47.1  2.5   7.1  0.3  2.7  0.1  not fire      \n",
       "3           25  89  13   2.5  28.6  1.3   6.9  0.0  1.7  0.0  not fire      \n",
       "4           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9  0.5  not fire      \n",
       "\n",
       "   Region  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    137\n",
       "0    106\n",
       "Name: Classes, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## categories in classes\n",
    "df_copy['Classes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Encoding of the categories in classes\n",
    "df_copy['Classes']=np.where(df_copy['Classes'].str.contains('not fire'),0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
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       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  RH  Ws  Rain  FFMC  DMC    DC  ISI  BUI  FWI  Classes  Region\n",
       "0           29  57  18   0.0  65.7  3.4   7.6  1.3  3.4  0.5        0       0\n",
       "1           29  61  13   1.3  64.4  4.1   7.6  1.0  3.9  0.4        0       0\n",
       "2           26  82  22  13.1  47.1  2.5   7.1  0.3  2.7  0.1        0       0\n",
       "3           25  89  13   2.5  28.6  1.3   6.9  0.0  1.7  0.0        0       0\n",
       "4           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9  0.5        0       0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>44.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>16.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>28</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>4.4</td>\n",
       "      <td>41.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27</td>\n",
       "      <td>87</td>\n",
       "      <td>29</td>\n",
       "      <td>0.5</td>\n",
       "      <td>45.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>7.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>24</td>\n",
       "      <td>54</td>\n",
       "      <td>18</td>\n",
       "      <td>0.1</td>\n",
       "      <td>79.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>15.2</td>\n",
       "      <td>1.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>24</td>\n",
       "      <td>64</td>\n",
       "      <td>15</td>\n",
       "      <td>0.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>3.8</td>\n",
       "      <td>16.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Temperature  RH  Ws  Rain  FFMC   DMC    DC  ISI   BUI  FWI  Classes  \\\n",
       "238           30  65  14   0.0  85.4  16.0  44.5  4.5  16.9  6.5        1   \n",
       "239           28  87  15   4.4  41.1   6.5   8.0  0.1   6.2  0.0        0   \n",
       "240           27  87  29   0.5  45.9   3.5   7.9  0.4   3.4  0.2        0   \n",
       "241           24  54  18   0.1  79.7   4.3  15.2  1.7   5.1  0.7        0   \n",
       "242           24  64  15   0.2  67.3   3.8  16.5  1.2   4.8  0.5        0   \n",
       "\n",
       "     Region  \n",
       "238       1  \n",
       "239       1  \n",
       "240       1  \n",
       "241       1  \n",
       "242       1  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    137\n",
       "0    106\n",
       "Name: Classes, dtype: int64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy['Classes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x1080 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot desnity plot for all features\n",
    "plt.style.use('seaborn')\n",
    "df_copy.hist(bins=50,figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Percentage for Pie Chart\n",
    "percentage=df_copy['Classes'].value_counts(normalize=True)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting piechart\n",
    "classlabels=[\"Fire\",\"Not Fire\"]\n",
    "plt.figure(figsize=(12,7))\n",
    "plt.pie(percentage,labels=classlabels,autopct='%1.1f%%')\n",
    "plt.title(\"Pie Chart of Classes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.651400</td>\n",
       "      <td>-0.284510</td>\n",
       "      <td>-0.326492</td>\n",
       "      <td>0.676568</td>\n",
       "      <td>0.485687</td>\n",
       "      <td>0.376284</td>\n",
       "      <td>0.603871</td>\n",
       "      <td>0.459789</td>\n",
       "      <td>0.566670</td>\n",
       "      <td>0.516015</td>\n",
       "      <td>0.269555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RH</th>\n",
       "      <td>-0.651400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.244048</td>\n",
       "      <td>0.222356</td>\n",
       "      <td>-0.644873</td>\n",
       "      <td>-0.408519</td>\n",
       "      <td>-0.226941</td>\n",
       "      <td>-0.686667</td>\n",
       "      <td>-0.353841</td>\n",
       "      <td>-0.580957</td>\n",
       "      <td>-0.432161</td>\n",
       "      <td>-0.402682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ws</th>\n",
       "      <td>-0.284510</td>\n",
       "      <td>0.244048</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171506</td>\n",
       "      <td>-0.166548</td>\n",
       "      <td>-0.000721</td>\n",
       "      <td>0.079135</td>\n",
       "      <td>0.008532</td>\n",
       "      <td>0.031438</td>\n",
       "      <td>0.032368</td>\n",
       "      <td>-0.069964</td>\n",
       "      <td>-0.181160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain</th>\n",
       "      <td>-0.326492</td>\n",
       "      <td>0.222356</td>\n",
       "      <td>0.171506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.543906</td>\n",
       "      <td>-0.288773</td>\n",
       "      <td>-0.298023</td>\n",
       "      <td>-0.347484</td>\n",
       "      <td>-0.299852</td>\n",
       "      <td>-0.324422</td>\n",
       "      <td>-0.379097</td>\n",
       "      <td>-0.040013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FFMC</th>\n",
       "      <td>0.676568</td>\n",
       "      <td>-0.644873</td>\n",
       "      <td>-0.166548</td>\n",
       "      <td>-0.543906</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.603608</td>\n",
       "      <td>0.507397</td>\n",
       "      <td>0.740007</td>\n",
       "      <td>0.592011</td>\n",
       "      <td>0.691132</td>\n",
       "      <td>0.769492</td>\n",
       "      <td>0.222241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DMC</th>\n",
       "      <td>0.485687</td>\n",
       "      <td>-0.408519</td>\n",
       "      <td>-0.000721</td>\n",
       "      <td>-0.288773</td>\n",
       "      <td>0.603608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.875925</td>\n",
       "      <td>0.680454</td>\n",
       "      <td>0.982248</td>\n",
       "      <td>0.875864</td>\n",
       "      <td>0.585658</td>\n",
       "      <td>0.192089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>0.376284</td>\n",
       "      <td>-0.226941</td>\n",
       "      <td>0.079135</td>\n",
       "      <td>-0.298023</td>\n",
       "      <td>0.507397</td>\n",
       "      <td>0.875925</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.508643</td>\n",
       "      <td>0.941988</td>\n",
       "      <td>0.739521</td>\n",
       "      <td>0.511123</td>\n",
       "      <td>-0.078734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ISI</th>\n",
       "      <td>0.603871</td>\n",
       "      <td>-0.686667</td>\n",
       "      <td>0.008532</td>\n",
       "      <td>-0.347484</td>\n",
       "      <td>0.740007</td>\n",
       "      <td>0.680454</td>\n",
       "      <td>0.508643</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.644093</td>\n",
       "      <td>0.922895</td>\n",
       "      <td>0.735197</td>\n",
       "      <td>0.263197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUI</th>\n",
       "      <td>0.459789</td>\n",
       "      <td>-0.353841</td>\n",
       "      <td>0.031438</td>\n",
       "      <td>-0.299852</td>\n",
       "      <td>0.592011</td>\n",
       "      <td>0.982248</td>\n",
       "      <td>0.941988</td>\n",
       "      <td>0.644093</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.857973</td>\n",
       "      <td>0.586639</td>\n",
       "      <td>0.089408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FWI</th>\n",
       "      <td>0.566670</td>\n",
       "      <td>-0.580957</td>\n",
       "      <td>0.032368</td>\n",
       "      <td>-0.324422</td>\n",
       "      <td>0.691132</td>\n",
       "      <td>0.875864</td>\n",
       "      <td>0.739521</td>\n",
       "      <td>0.922895</td>\n",
       "      <td>0.857973</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.719216</td>\n",
       "      <td>0.197102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Classes</th>\n",
       "      <td>0.516015</td>\n",
       "      <td>-0.432161</td>\n",
       "      <td>-0.069964</td>\n",
       "      <td>-0.379097</td>\n",
       "      <td>0.769492</td>\n",
       "      <td>0.585658</td>\n",
       "      <td>0.511123</td>\n",
       "      <td>0.735197</td>\n",
       "      <td>0.586639</td>\n",
       "      <td>0.719216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.162347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>0.269555</td>\n",
       "      <td>-0.402682</td>\n",
       "      <td>-0.181160</td>\n",
       "      <td>-0.040013</td>\n",
       "      <td>0.222241</td>\n",
       "      <td>0.192089</td>\n",
       "      <td>-0.078734</td>\n",
       "      <td>0.263197</td>\n",
       "      <td>0.089408</td>\n",
       "      <td>0.197102</td>\n",
       "      <td>0.162347</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Temperature        RH        Ws      Rain      FFMC       DMC  \\\n",
       "Temperature     1.000000 -0.651400 -0.284510 -0.326492  0.676568  0.485687   \n",
       "RH             -0.651400  1.000000  0.244048  0.222356 -0.644873 -0.408519   \n",
       "Ws             -0.284510  0.244048  1.000000  0.171506 -0.166548 -0.000721   \n",
       "Rain           -0.326492  0.222356  0.171506  1.000000 -0.543906 -0.288773   \n",
       "FFMC            0.676568 -0.644873 -0.166548 -0.543906  1.000000  0.603608   \n",
       "DMC             0.485687 -0.408519 -0.000721 -0.288773  0.603608  1.000000   \n",
       "DC              0.376284 -0.226941  0.079135 -0.298023  0.507397  0.875925   \n",
       "ISI             0.603871 -0.686667  0.008532 -0.347484  0.740007  0.680454   \n",
       "BUI             0.459789 -0.353841  0.031438 -0.299852  0.592011  0.982248   \n",
       "FWI             0.566670 -0.580957  0.032368 -0.324422  0.691132  0.875864   \n",
       "Classes         0.516015 -0.432161 -0.069964 -0.379097  0.769492  0.585658   \n",
       "Region          0.269555 -0.402682 -0.181160 -0.040013  0.222241  0.192089   \n",
       "\n",
       "                   DC       ISI       BUI       FWI   Classes    Region  \n",
       "Temperature  0.376284  0.603871  0.459789  0.566670  0.516015  0.269555  \n",
       "RH          -0.226941 -0.686667 -0.353841 -0.580957 -0.432161 -0.402682  \n",
       "Ws           0.079135  0.008532  0.031438  0.032368 -0.069964 -0.181160  \n",
       "Rain        -0.298023 -0.347484 -0.299852 -0.324422 -0.379097 -0.040013  \n",
       "FFMC         0.507397  0.740007  0.592011  0.691132  0.769492  0.222241  \n",
       "DMC          0.875925  0.680454  0.982248  0.875864  0.585658  0.192089  \n",
       "DC           1.000000  0.508643  0.941988  0.739521  0.511123 -0.078734  \n",
       "ISI          0.508643  1.000000  0.644093  0.922895  0.735197  0.263197  \n",
       "BUI          0.941988  0.644093  1.000000  0.857973  0.586639  0.089408  \n",
       "FWI          0.739521  0.922895  0.857973  1.000000  0.719216  0.197102  \n",
       "Classes      0.511123  0.735197  0.586639  0.719216  1.000000  0.162347  \n",
       "Region      -0.078734  0.263197  0.089408  0.197102  0.162347  1.000000  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\win10\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='FWI'>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Box Plots\n",
    "sns.boxplot(df['FWI'],color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>RH</th>\n",
       "      <th>Ws</th>\n",
       "      <th>Rain</th>\n",
       "      <th>FFMC</th>\n",
       "      <th>DMC</th>\n",
       "      <th>DC</th>\n",
       "      <th>ISI</th>\n",
       "      <th>BUI</th>\n",
       "      <th>FWI</th>\n",
       "      <th>Classes</th>\n",
       "      <th>Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "      <td>61</td>\n",
       "      <td>13</td>\n",
       "      <td>1.3</td>\n",
       "      <td>64.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>7.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>26</td>\n",
       "      <td>82</td>\n",
       "      <td>22</td>\n",
       "      <td>13.1</td>\n",
       "      <td>47.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>7.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>0.1</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>25</td>\n",
       "      <td>89</td>\n",
       "      <td>13</td>\n",
       "      <td>2.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2012</td>\n",
       "      <td>27</td>\n",
       "      <td>77</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.5</td>\n",
       "      <td>not fire</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day  month  year  Temperature  RH  Ws  Rain  FFMC  DMC    DC  ISI  BUI  \\\n",
       "0    1      6  2012           29  57  18   0.0  65.7  3.4   7.6  1.3  3.4   \n",
       "1    2      6  2012           29  61  13   1.3  64.4  4.1   7.6  1.0  3.9   \n",
       "2    3      6  2012           26  82  22  13.1  47.1  2.5   7.1  0.3  2.7   \n",
       "3    4      6  2012           25  89  13   2.5  28.6  1.3   6.9  0.0  1.7   \n",
       "4    5      6  2012           27  77  16   0.0  64.8  3.0  14.2  1.2  3.9   \n",
       "\n",
       "   FWI      Classes  Region  \n",
       "0  0.5  not fire          0  \n",
       "1  0.4  not fire          0  \n",
       "2  0.1  not fire          0  \n",
       "3  0.0  not fire          0  \n",
       "4  0.5  not fire          0  "
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Classes']=np.where(df['Classes'].str.contains('not fire'),'not fire','fire')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fire Analysis of Sidi- Bel Regions')"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Monthly Fire Analysis\n",
    "dftemp=df.loc[df['Region']==1]\n",
    "plt.subplots(figsize=(13,6))\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='month',hue='Classes',data=df)\n",
    "plt.ylabel('Number of Fires',weight='bold')\n",
    "plt.xlabel('Months',weight='bold')\n",
    "plt.title(\"Fire Analysis of Sidi- Bel Regions\",weight='bold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Fire Analysis of Brjaia Regions')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Monthly Fire Analysis\n",
    "dftemp=df.loc[df['Region']==0]\n",
    "plt.subplots(figsize=(13,6))\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='month',hue='Classes',data=df)\n",
    "plt.ylabel('Number of Fires',weight='bold')\n",
    "plt.xlabel('Months',weight='bold')\n",
    "plt.title(\"Fire Analysis of Brjaia Regions\",weight='bold')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its observed that August and September had the most number of forest fires for both regions. And from the above plot of months, we can understand few things\n",
    "\n",
    "Most of the fires happened in August and very high Fires happened in only 3 months - June, July and August.\n",
    "\n",
    "Less Fires was on September"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
